Universiti Teknologi Malaysia Institutional Repository

Emg-based facial gesture recognition through versatile elliptic basis function neural network

Hamedi, Mahyar and Shaikh Salleh, Shaikh Hussain and Astaraki, Mehdi R. and Mohd. Noor, Alias (2013) Emg-based facial gesture recognition through versatile elliptic basis function neural network. Biomedical Engineering Online, 12 (1). ISSN 1475-925X


Official URL: http://dx.doi.org/10.1186/1475-925X-12-73


Recently, the recognition of different facial gestures using facial neuromuscular activities has been proposed for human machine interfacing applications. Facial electromyograms (EMGs) analysis is a complicated field in biomedical signal processing where accuracy and low computational cost are significant concerns. In this paper, a very fast versatile elliptic basis function neural network (VEBFNN) was proposed to classify different facial gestures. The effectiveness of different facial EMG time-domain features was also explored to introduce the most discriminating.Methods: In this study, EMGs of ten facial gestures were recorded from ten subjects using three pairs of surface electrodes in a bi-polar configuration. The signals were filtered and segmented into distinct portions prior to feature extraction. Ten different time-domain features, namely, Integrated EMG, Mean Absolute Value, Mean Absolute Value Slope, Maximum Peak Value, Root Mean Square, Simple Square Integral, Variance, Mean Value, Wave Length, and Sign Slope Changes were extracted from the EMGs. The statistical relationships between these features were investigated by Mutual Information measure. Then, the feature combinations including two to ten single features were formed based on the feature rankings appointed by Minimum-Redundancy-Maximum-Relevance (MRMR) and Recognition Accuracy (RA) criteria. In the last step, VEBFNN was employed to classify the facial gestures. The effectiveness of single features as well as the feature sets on the system performance was examined by considering the two major metrics, recognition accuracy and training time. Finally, the proposed classifier was assessed and compared with conventional methods support vector machines and multilayer perceptron neural network.Results: The average classification results showed that the best performance for recognizing facial gestures among all single/multi-features was achieved by Maximum Peak Value with 87.1% accuracy. Moreover, the results proved a very fast procedure since the training time during classification via VEBFNN was 0.105 seconds. It was also indicated that MRMR was not a proper criterion to be used for making more effective feature sets in comparison with RA.Conclusions: This work was accomplished by introducing the most discriminating facial EMG time-domain feature for the recognition of different facial gestures; and suggesting VEBFNN as a promising method in EMG-based facial gesture classification to be used for designing interfaces in human machine interaction systems

Item Type:Article
Uncontrolled Keywords:electromyogram, facial gesture recognition, facial neural activity, feature extraction, human machine interface
Subjects:R Medicine > R Medicine (General)
Divisions:Biosciences and Medical Engineering
ID Code:49000
Deposited By: Siti Nor Hashidah Zakaria
Deposited On:02 Dec 2015 02:10
Last Modified:14 Oct 2018 08:21

Repository Staff Only: item control page